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"Classical" First Order (FO) algorithms of convex optimization, such as Mirror Descent algorithm or Nesterov's optimal algorithm of smooth convex optimization, are well known to have optimal (theoretical) complexity estimates which do not depend on the problem dimension.
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A method for unconstrained convex minimization problem with the rate of convergence O ¯ ( 1 / k 2 ) \underline{O}(1/k^{2})
Y. Nesterov · 1983
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Uncovering shared structures in multiclass classification
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Dual extrapolation and its applications to solving variational inequalities and related problems
Y. Nesterov · 2007
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Primal-dual subgradient methods for convex problems
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Dualization of signal recovery problems
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Accuracy certificates for computational problems with convex structure
A. Nemirovski, S. Onn, and U. G. Rothblum · 2010
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High dimensional covariance matrix estimation in approximate factor models
J. Fan, Y. Liao, and M. Mincheva · 2011
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Y. Nesterov · 2004
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Non-euclidean restricted memory level method for large-scale convex optimization
A. Ben-Tal and A. Nemirovski · 2005
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Smooth minimization of non-smooth functions
Y. Nesterov · 2005
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First order methods for nonsmooth large-scale convex minimization, i: General purpose methods; ii: Utilizing problem’s structure
A. Juditsky and A. Nemirovski · 2011
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Some first order algorithms for ℓ 1 \ell_{1} /nuclear norm minimization
Y. Nesterov and A. Nemirovski · 2013
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